“The impact of ethical and
participative leadership on
innovative work behaviour
in tech firms:
The role of self-efficacy and creative process engagement
”
Anastasopoulou Kyriaki & Angelis Dimitrios
Supervisor Philippe Rouchy Karlskrona, Sweden September 2020
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This thesis is submitted to the Department of Industrial Economics at Blekinge Institute of Technology in partial fulfilment of the requirements for the Degree of Master of Science in Industrial Economics and Management. The thesis is awarded 15 ECTS credits.
The author(s) declare(s) that they have completed the thesis work independently. All external sources are cited and listed under the References section. The thesis work has not been submitted in the same or similar form to any other institution(s) as part of another examination or degree.
Author information:
Kyriaki Anastasopoulou kirkianast@gmail.com Dimitrios Angelisdimitriosangelis@yahoo.com
Department of Industrial Economics Blekinge Institute of Technology SE-371 79 Karlskrona, Sweden Website: www.bth.se
Telephone: +46 455 38 50 00 Fax: +46 455 38 50 57
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We would like to thank and express our sincere gratitude to our supervisor, Professor Philippe Rouchy for his guidance and support throughout the time of conducting this research.
Further, we would also like to express our appreciation to Mr. Anders Wrenne for his coordination and guidance for this Master Thesis course.
The same appreciation is shown to all our professors and tutors from BTH University who imparted their knowledge to us.
We also thank our families, friends and colleagues for their support. Finally, special thanks must go to all the professionals who participated in our survey whom without this study would not have been possible.
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Background: Nowadays, companies from the technological sector confront extreme competition and it is always a challenge for leadership teams to increase competitiveness. This study aims to investigate innovation advancement within tech companies in an international context from the leadership and management incentives point of view. Leadership plays a vital role in giving direction to the path an organization should follow. It is of significant interest to examine how leadership can drive an organization to innovative thinking. Different approaches and leadership styles can be adopted and practiced by leaders to produce different outcomes on employees’ creative culture. Additionally, individual characteristics of the employees such as self-efficacy and creativity may allow the innovative behaviours to strive and create a workplace culture that is inducing innovative output. Innovative work behaviour is becoming more popular or even mandatory within several firms in the technological sector in contrast to previous decades.
Purpose: The purpose of this study is to explore how ethical leadership and participative leadership style can affect innovative work behaviour and to examine if the creative process engagement and self-efficacy lead to enhanced innovative work behaviour.
Methodology: For this thesis a quantitative approach for data collection, as well as data analysis is used. This study is based on a SEM model which contains close-ended questions that were answered through a self-administrated questionnaire. The survey was answered by employees working at companies from the field of technology, covering different positions. A total of 177 respondents answered the questionnaire, and the results were analysed both in quantitative and qualitative ways. IBM SPSS software was used for the statistical analysis, and AMOS 26 for the Structural Equation Modeling tests. Results: The results of the statistical analysis performed unveiled that the aspects of ethical and participative leadership can positively affect creativity and innovation and that self-efficacy can positively relate to creative process engagement.
Conclusion: This study contributes in showing that two positive ways of management, ethical and participative can be introduced by leaders that are interested in increasing creativeness and innovation at work; it also shows that for the sample tested, ethical and participative leadership does not necessarily has a major effect on employee’s self-efficacy.
Delimitations: The geographical locations, the time and sample size, the choice of participating organizations, and the framework designed for the evaluation of the theoretical problem are considered as limitations for this study. This research is mainly limited to professionals working in the technological sector and the study is restricted in time since the participants had to answer in a certain time frame. To conclude, the sample size of the survey even though is satisfactory for its intended use, could be higher. Keywords: Innovative Work Behaviour, Self-Efficacy, Creative Process Engagement, Creativity, Ethical Leadership, Participative Leadership, Leadership Styles.
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Table of contents
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List of abbreviations
AGFI Adjusted Goodness-of-fit
AVE Average Variance Extracted
ASV Average Shared Variance
CE Creative Process Engagement
CFA Confirmatory Factor Analysis
CFI Comparative Fit Index
CMIN Chi-square Value
CMIN/DF Relative Chi-square
CR Critical Ratio
EFA Exploratory Factor Analysis
EL Ethical Leadership
GFI Goodness of Fit Index
GOF Goodness of-fit
IBM International Business Machines IW Innovative Work Behaviour
MSV Maximum Shared Variance
NFI Normed Fit Index
PhD Doctor of Philosophy
PL Participative Leadership
RMSEA Root Mean Square Error of Approximation R&D Research and Development
UK United Kingdom
SEM Structural Equation Modeling SE Self-Efficacy
SPSS Statistical Package for the Social Sciences Ȝ Lambda or Factor Loading
1.
Introduction
Globalization, technological developments, and harsh market environments are some of the several reasons that have driven businesses to give a great emphasis on innovation (Akram, et al., 2016). Due to swift technological advancements, service sector organizations in particular are examining innovation in order to survive the rivalry (Archibugi, et al., 2013).
The leadership of a firm must follow an optimistic method to accomplish innovation, as innovation cannot be achieved in a negative environment (Donate, et al., 2015). Participative leadership style can be categorized as a positive leadership style in which personnel are given the prospect of engagement in decision making and problem-solving through inspiration, support and encouragement (Somech & Wenderow, 2006). Participative leaders provide teams with accountability by involving them in decision making activities (Sauer, 2011). A participative leader may improve employees’ efficiency, organizational citizenship behaviour and many other positive behaviours (Miao, et al., 2014). Participative leadership embraces the teamwork approach (DuBrin, 2013). Research has shown that low performing teams are often governed by the team leader, although well performing teams are identified to embrace shared leadership (DuBrin, 2013). Participative leadership provides the experience of team spirit, and offers the warmth of a friendly professional environment.
Considering the developing devotion applied to corporate social responsibilities and business ethics, leaders nowadays are needed to work ethically. Therefore, ethical leadership has been at the attention of both academia and industry in the past years (Kalshoven, et al., 2011). In the process of making, endorsing and realizing new concepts, methods or processes, many risks, complications, clashes, and even ethical dilemmas may be encountered; this shows that ethical leadership that highlighted principles, social responsibility, independence, and people direction (Brown & Trevin˜o, 2006) can be a possible interpretation of innovative work behaviour. Ethics examine moral responsibilities or differentiate the right from wrong (DuBrin, 2013). According to DuBrin, “An ethical leader is honest and trustworthy, therefore has integrity” (DuBrin, 2013). It is vital to consider integrity as an important factor which needs to be adopted by all professional cultures; it is believed that integrity can give positive results with regards to employee performance. Integrity signifies adherence to logical morals; it contemplates engaging in the right beliefs irrespective of psychological or public constraint (DuBrin, 2013).
By conducting this study, it is intended to examine how participative and ethical leadership impacts employee’s innovative work behaviour and the primary mechanisms.
Problem discussion
The theoretical problem of the research is to investigate the impact of ethical and participative leadership on employees’ self-efficacy and creative process engagement, and subsequently on their innovative work behaviour.
Studies have been performed to demonstrate relationships between ethical leadership or participative leadership style and their impact on innovative work behaviour. Since leadership is made of knowledge of personal, decision making and other aspects of labour activities that are difficult to measure, this study investigates those relations in order to figure out what is the influence on innovative work behaviour of both the moral aspects of ethical leadership and participative leadership style.
Problem formulation and purpose
This thesis examines how different types of leadership (ethical and participative) affect the employee’s behaviour towards innovative work. In order to investigate those aspects, a SEM (Structural Equation Modeling) model with five constructs is used. This allows to study the relationships between elements of behaviour that other studies cannot perform. Firstly, it analyses two main inputs (i) ethical leadership and (ii) participative leadership style, that reveal to be in significant relationship with secondly - two important creative behaviour mediators which are (iii) self-efficacy and (iv) creative process
engagement, and finally, it examines if these inputs and mediators can positively influence the one output which is the (v) innovative work behaviour.
Delimitations
To write this thesis, a sample of 177 professionals in the technological sector was collected. The study was limited with samples from technological sectors, mainly Oil & Gas and Shipping. Some respondents were from construction and advertising industry firms, but still within the technological sector. It was intended to examine professionals’ answers from mainly Greece and the UK, but professionals from some other countries have participated. The survey embraces selected employees of different level of seniority and profession, from management to engineering, finance, administration, etc.
In addition, the sample size has been gathered during a strict one-week time period. The creation of the questionnaire and its distribution to the population took 2 months. The participants had a limited time to answer the questionnaires due to a strict schedule and therefore, the collection period was limited to one week. It has resulted in some intensive activities to contact and insure following-up of respondents. This means that more time would have helped this research in gathering a larger sampling. The time spent to gather all the data was considered as the most possible and effective use of time in order to deliver this study.
Thesis Structure
The thesis uses several chapters, including: (1) Introduction; (2) Theory; (3) Research Methodology; (4) Survey Descriptive Results; (5) Empirical Findings / Statistical Analysis; (6) Analysis and Discussion; and (7) Conclusion. The theory chapter consists of two main parts, the literature review and the theoretical framework. The literature review presents the background work performed and explains the pre-requisites for this research, categorizes the subject being investigated, highlights the specific area of interest, and at the same, time emphasizes their theoretical and practical significance. The theoretical framework explains the area of interest, presents a Structural Equation Modeling (SEM), and analyses the hypotheses created for the modelled constructs. The research methodology section gives a detailed description of the research design adopted in the study, and explains analytically how the participants were sampled. The survey descriptive results section presents the data collected, and provides the demographics data which contribute in the theoretical analysis. The empirical findings /statistical analysis section uses statistical tools to analyse the collected data from the sample, elaborates on all steps taken to derive a valid model, and the final analysis and discussion section concludes to the final structural model, and explains what the results obtained from the analyses mean. The conclusion aims to discuss the findings and the analysis results, and intends to answer the research question, to highlight the outcomes of the research and give suggestions for future work.
2.
Theory
This chapter underlines the literature review which defines the theoretical framework of the thesis. It provides an overview of the ethical and participative leadership, and emphasizes their relation to innovative work behaviour. A high-level overview of self-efficacy and creative process engagement is also provided to explain their influence on innovative work behaviour. This chapter constitutes the theoretical framework upon which the SEM (Structural Equation Modelling) is based. It contains the constructs and hypotheses for measuring and testing how leadership components affect the innovative work behaviour.
Literature Review
Studies on ethical or participative leadership exist but are not necessarily combined in scientific articles to tackle the issue of innovative output in the workplace. Therefore, this study focuses on ethical leadership or participative leadership style, and their impact specifically related to employees’ performance and innovative work behaviour.
The current study links ethical leadership and participative leadership style with the self-efficacy, creative process engagement and innovative work behaviour. This combination has not been investigated by prior studies. The major rationale behind ethical and participative leadership is to appreciate the role positive leadership plays, with employee’s output seen as self-efficacy, creative process engagement and innovative work behaviour. It is of great importance to examine these two different leadership styles together, because each of them touches upon different areas of technological development. The participative management is generally fitting in a high technology environment whereby the workforce is high educated and cannot be managed with simple top-down principles. The ethical leadership is covering industries that imply either ethical principle to deal with others (such as medicine) or the environment (such as oil& gas and shipping).It can be argued that these two positive styles of leadership can be applied almost everywhere as even in the firm environments of manufacturing, labour could participate in a production line improvement, or focus in working ethically by respecting others, the law and the environment.
Innovation at Work
Lately, innovative work behaviour has presented a compelling importance because of the continuous competition and the fast-changing global market (Shanker, et al., 2017). Work innovation is related with change or altering behaviour that anticipates to adjust the tactic that a business works (Madrid, et al., 2014). Innovative work behaviour expresses intention of introducing new products, services, concepts, processes or procedures, as well as development and implementation of new divisions or establishments in an organisation (De Jong & Den Hartog, 2008). Innovative work behaviour requires changing, so people’s readiness for change contributes to the outcome of innovative achievement (Kwahk & Lee, 2008). Therefore, since prior studies have emphasized the importance of the innovation at work, this study will contribute further on the examination of how ethical and participative leadership styles can endorse employee’s innovativeness at work.
Influence of Leadership Methods
Even if the leadership literature has been examined through several prior studies, there is not a common understanding and agreement on how leadership works and what it means (Akram, et al., 2016). Although there is a lot of research with respect to managers’ leadership styles, there is still a lack of understanding which are the factors that lead to innovative work behaviour (Akram, et al., 2016), and there is also limited knowledge about the outcomes of this novel way of employees’ behaviour (Trivisonno & Barling, 2016). Exceptionally, another study presented how ethical leadership improved employees’ job performance from enhancing motivation (Piccolo, et al., 2010). This study states that ethical leadership strengthens employees’ elemental motivation by arranging the neutral and individual job aspects. It provides a better viewpoint in the encouragement aspect that ethical leadership offers and
the possible impact on the employees’ intrinsic motivation. The current study outlines how employee self-efficacy is affected from ethical leadership but also from participative leadership style.
Another paper examining if participative leadership promotes the innovative work behaviour proved that when leaders practice this style of management, the personnel feel more dedicated to adjust and subsequently their innovative behaviour increases (Fatima, et al., 2017). The relevant study intends to test different mediating mechanisms than those used in the aforementioned study to further explain the relationship between participative leadership style and innovative work behaviour. In addition, it is vital to take into consideration that the diversity of personality will affect the outcomes.
This thesis links leadership methods to innovative work behaviour from an employees’ creativeness and self-belief perspective. A model is proposed and in order to contribute to the existing participative and ethical leadership literature in several ways: (1) highlighting the employees’ self-efficacy – shows the positive correlation of the leaders practicing participative and ethical leadership with the subordinates in order to innovate; (2) emphasizing on employees’ creative process engagement – reliance on the process of how participative and ethical leadership influences employees’ innovative work behaviour through the mediation of creativity engagement.
Importance of Employee Creativity
Considering the continuously changing environments, high competition, and technological changes, managers tend to motivate their employees to be more creative (Shalley & Gilson, 2004). Prior studies have presented that employee creative process engagement can positively affect organizational novelty, survival, and effectiveness (Shalley, et al., 2004). This study examines the creative process engagement by employees and its correlation with both participative and ethical leadership practices. In this thesis, creative process engagement is considered as a mediator that can enhance innovative work behaviour. According to a previous study, for creativity to exist, managers should support and promote it, since they are those that know better than anyone else within an organization which employee could improve creativity; managers can also affect the occurrence of creativity (Zhang & Bartol, 2010). Within the same study, it is also stated that enhancing leadership includes empowering employees’ motivation and therefore, this action may lead to a positive impact on their creativity. This paper by Zhang & Bartol (2010), leaves the margin for further research on how the individual variable of self-efficacy interrelates with creativity. Hence, the present study will try to examine if self-efficacy of employees has a positive relationship with the creative process and promotes the generation of innovative and valuable ideas.
Theoretical Framework
To investigate the impact of ethical and participative leadership in innovative work behaviour, the theoretical framework is intended to test if self-efficacy and creative process engagement positively influence the innovative work behaviour. The research framework is illustrated in Figure 1.
Figure 1 Research Framework
Ethical Leadership
Ethical leadership is defined as an effort from the leader to promote an appropriate conduct through individual actions and interpersonal relationships to his/her followers, through an existing communication between them and decision making (Brown, et al., 2005). The ethical leader is therefore implied to have honesty, honour, unselfishness, reliability, cooperative incentive, and righteousness as a moral person (Brown & Trevin˜o, 2006). The moral manager influences his employees’ approaches and actions via the practicing of his ethical leadership behaviour (Trevin˜o & Brown, 2004). Ethical managers/leaders are keen on impacting positively the work of the others, including groups, organizations, and even entire societies (Brown & Trevin˜o, 2006). Their supporters are more interested in producing new concepts for the reason of achieving the expected goals. Since the ethical leaders are characterized by positive traits and are committed to the organization (De Hoogh & Den Hartog, 2008), their followers are feeling safe to express their new ideas and share their knowledge with their colleagues (Janssen, 2003).
Ethical leadership is crucial for organizations because it helps them to decrease business costs by engaging fairness and morality towards their employees and all other stakeholders (Thomas, et al., 2004). Prior research has investigated ethical leadership’s positive role, since it decreases the destructive behaviours of employees and discourages the possible immoral practices (Mayer, et al., 2009).
Hypothesis 1: Ethical leadership is positively correlated to creative process engagement. Hypothesis 2: Ethical leadership is positively correlated to self-efficacy.
Participative Leadership
Participative leadership is perceived as a positive style of leadership, and it is defined as an approach in which the manager allows his/her peers to participate in decision making (Kahai, et al., 1997). The leader creates opportunities to subordinates for contributing in problem-solving by the use of encouragement (Somech, 2006). A sense of responsibility is created on subordinates when participative leaders allow them to have a role in decision making (Sauer, 2011). Participative leadership application has a high probability in producing good performances and various positive behaviours. Another empirical study has shown that the impact of participative leadership on work results in different industrial and cultural fields (Kahai, et al., 1997). It has been shown that participative leadership increases job performance (Yousef, 2000). Participative leaders do not enforce decisions on subordinates. However, they welcome employees’ suggestions and they take decisions based on consensus (Somech, 2006). Employees by this way feel honoured and privileged, and at the same time motivated that their leaders respect them, and treat them equally with the rest of the organization’s
members. The observation of the leader’s positive behaviour in the work environment on a repeated basis makes employees to adopt such a behaviour as well. These assumptions allow the creation of the following hypotheses:
Hypothesis 3: Participative leadership is positively correlated to creative process engagement. Hypothesis 4: Participative leadership is positively correlated to self-efficacy.
Creative Process Engagement
Creative process engagement is defined as employees’ involvement in methods and processes relevant to creativity (Zhang & Bartol, 2010). A pure indication of creative process engagement is when employees are involved or engaged in intellectual processes relevant to creativity. The Creative process engagement has three critical dimensions (Reiter-Palmon & Illies, 2004). The first dimension of creativity process is when the employee identifies the issue, objectives, processes, limitations, and information for the problem to be solved. The time spent for this first stage is key for the quality and validity of problem’s resolution (Reiter-Palmon & Illies, 2004). When the problem is identified, the employee continues by collecting and processing relevant information (Zhang & Bartol, 2010). The second stage associates with searching information relevant with the identified problem in order to be better understood (Mumford, 2000). The time spent on searching information positively affects the solution’s quality, and is likely for the creativity to be increased. Finally, the last dimension in the creativity process is to contemplate and create concepts related to the obstacle, while incorporating the relevant information (Zhang & Bartol, 2010). Combining and organizing all the gathered information creates a new way of understanding, and the applications and implications of this new understanding obviously conclude to a set of new ideas (Mumford, 2000). Creative process engagement helps the employees to engage in creative activities and remain committed throughout the creative process, until new and useful ideas are realized (Hennessey & Amabile, 2010). According to the above arguments, the following hypothesis is presented:
Hypothesis 5: Creative process engagement is positively related to innovative work behaviour.
Self-efficacy
Self-efficacy has been formed as an idea in social learning theory, and firstly shaped in the social psychology grounds (Bandura, 2000). Bandura suggested that self-efficacy is crucial in tasks’ performance because it influences humans’ decisions, achievements and stamina. Based on this theory, individuals can self-control and self-regulate; this means that their emotions and behaviours can be controlled and their destiny determined at their will. These mental processes relate significantly with people’s behaviour. Continuing Bandura’s idea, being in the possession of knowledge, skills and prior accomplishments are not appropriate predictors of the future performance. However, people’s belief in their own capabilities to accomplish different tasks and jobs is effective on their performance quality (Bandura, 1997). Humans learn the standards of behaviour via direct modelling or verbal encouragement; helping others to believe in their capabilities is reinforcing their motivational and behavioural standards (De Hoogh & Den Hartog, 2008). To conclude, the aforementioned lead to the following hypothesis:
Hypothesis 6: Self-efficacy is positively correlated to innovative work behaviour.
Innovative Work Behaviour
Innovative work behaviour is recognizing the problems, initiating and incorporating (as an employee, or a member of a group, or organization) new and practical ideas concerning products, services, and work methods, as well as having the adequate behaviour to establish and carry out these ideas aiming to enhance personally or in terms of business (De Jong & Den Hartog, 2007). The last years, the great value of innovative work behaviour has been shown due to the continuously changing global market as well as the expanded competition (Shanker, et al., 2017). According to current trends in recruitment, most of organizations check applicants’ cognitive and innovative abilities in order to assure that their
workforce will act with innovative work behaviour (Delgadová, et al., 2017). Organizations proceed in changing their leadership styles for the reason of the vital role leaders play in promoting employees’ efficiency (Strom, et al., 2014).
Generally, it is hypothesized that all constructs positively affect Innovative Work Behaviour.
Summary
The presented literature review summarises the significant findings from previous researches which are essential for this thesis. Several relative studies have been found for the variables examined (“Innovative Work Behaviour”, “Ethical Leadership”, “Participative Leadership”, “Self-Efficacy” and “Creative Process Engagement”). Previous studies have considered some of these variables combined together, but no study has examined all the five derived constructs at the same time in a single model. As already referred through the relevant thesis, both Participative and Ethical Leadership will be examined to further explain their relationship with innovative work behaviour. It has to be noted, that in reality, different types of personality affect the findings/results of this research. In addition, this research study was also conducted to examine if employees high self-efficacy and creative process engagement can produce innovation and whether the aforementioned management styles influence the mediators positively.
3.
Research Method
Overview
This study is based on a quantitative methodological approach called SEM, Structural Equation Modeling. It is used because it evaluates and measures relationships between a set of variables and indicators, and provides evaluation of which ones are reliable for the underlying theory tested. This chapter summarizes the methodology used to pull together the SEM model and details the various constructs.
Structural Equation Modeling (SEM) can explain the relationships among multiple variables, which are expressed in equations that characterize all of the relationships among dependent and independent variables (constructs) of in the analysis. Constructs are factors signified by multiple variables (Hair, et al., 2014). SEM examines the relationships of dependent and independent variables in the same theory (Hair, et al., 2014).
A SEM model expresses a theory as a systematic set of relationships which explain innovative work behaviour reliably and specifically. A SEM model introduces a measurement model (how variables represent constructs) and the structural model (constructs interrelationships); a structural model includes structural relationships between latent constructs, these are: participative leadership style, ethical leadership, creative work engagement, self-efficacy and innovative work behaviour.(Hair, et al., 2014). According to Hair et al, “a latent construct cannot be measured directly but can be represented or measured by one or more variables (indicators). In combination, the answers to these questions give a reasonably accurate measure of the latent construct (attitude) for an individual." In our thesis, the indicators are represented by the questions as explained later in this chapter, in accordance with SEM theory.
SEM measurement theory consists of a “Series of relationships that suggest how measured variables represent a construct not measured directly (latent). A measurement theory can be represented by a series of regression-like equations mathematically relating a factor (construct) to the measured variables" (Hair, et al., 2014).
The Six Stages of SEM
SEM is used to evaluate by data representation the realization feasibility of the theory. According to Hair et al, “SEM can be defined by a six-stage decision process. This process reflects the unique terminology and procedures of SEM.”
The six stages are listed below (Hair, et al., 2014): ¾ Stage 1: Defining individual constructs
¾ Stage 2: Developing the overall measurement model ¾ Stage 3: Designing a study to produce empirical results ¾ Stage 4: Assessing the measurement model validity ¾ Stage 5: Specifying the structural model
Stage 1: Defining Individual Constructs
A decent measurement theory is essential to get useful outcomes from SEM (Hair, et al., 2014). Considerable time must be devoted at an initial stage of the research process to guarantee valid decisions are taken; defining the constructs is an important step in the research method.
The Operationalization of the constructs is an important follow up step, by selecting its measurement scale and type. Constructs of the model can be defined and operationalized as similarly done in previous studies. This study’s literature research regarding constructs, identified scales that have previously performed well. Past research is an effective way to keep the standard of quality of new studies. Regarding data collection, this thesis uses questionnaires; it is common for past questions from parallel research to be incorporated in questionnaires (Hair Jnr., et al., 2010).
This study’s constructs are listed below: ¾ Ethical Leadership (EL) ¾ Participative Leadership (PL)
¾ Self-Efficacy (SE)
¾ Creative Process Engagement (CE) ¾ Innovative Work Behaviour (IW)
Stage 2: Developing the Overall Measurement Model
The model uses SEM and comprises of both a measurement model and a structural model. By using a survey, the variables are measured. In SEM, the constructs are non-observable or latent factors that are expressed by a variate that contains multiple variables. Using this methodology, several variables incorporate mathematically a construct’s representation (Hair, et al., 2014).
After the constructs were specified, the measurement model was then created. All constructs used in the model were defined together with the relationships between constructs, or else, the hypotheses. In this model analysis, the constructs were considered unidimensional, which means that each indicator is related to only one construct, thus cross-loadings are set to zero. The number of indicators per construct is five for 4 constructs and 7 for one construct. This results in a total of twenty-seven indicators. It is worth mentioning that more indicators do not automatically produce a better result. More indicators can produce greater reliability results, but they need a larger sample (Hair, et al., 2014).
Path diagrams are advantageous in defining and communicating the theoretical model structure to the program (Hair, et al., 2014). The model parameters specified (as shown in Figure 2) could be estimated since the theoretical model structure was finalised. The path diagram created consists of 27 indicators and is shown in Figure 2. The 27 questions created to represent the 27 indicators were part of the 2nd
stage of SEM.
Our thesis has concluded in using SEM as it is found to be the best method to assess management styles and their influence to work behaviours. According to Hair, "Researchers are attracted to SEM because it provides a conceptually appealing way to test theory. If a researcher can express a theory in terms of relationships among measured variables and latent constructs (variates), then SEM will assess how well the theory fits reality as represented by data."
Figure 2 Path Diagram
Stage 3: Designing a Study to Produce Empirical Results
After creating the theoretical model comprising of its constructs and indicators, the study’s design and estimation issues must come into consideration. The analysis emphasises on three main points, “(1) the covariances or correlations of data to be studied; (2) the consequence for missing data and its provisions and (3) the effect of sample size” (Hair, et al., 2014).
The theory of communality is a suitable method for the purposes of reviewing the sample size issue; communalities signify the average variation among the indicator variables described by the model (Hair, et al., 2014). The selected model comprises of five constructs, and this study examines their communalities.
This research aims to produce a minimum sample size of 150. According to Hair et al, a sample size of 150 is suitable for models with seven constructs or less and modest communalities (i.e. 0.5), and also when there aren’t any under identified constructs.
Chapter 4 outlines the results from the 27 questions and forms part of Stage 3 of SEM. In addition, Chapter 5, Empirical Results / Statistical Analysis, is also part of the 3rd stage of SEM and notably
includes Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA).
Stage 4: Assessing Measurement Model Validity
Validity checks defined the process of this research study as the study’s main statistical goal was to provide a valid and reliable model. All the proposed models tested, with their validity checks to be a top priority, and all necessary statistical enhancement methods were applied to achieve a valid model. The validity of the SEM model was examined, by checking the fitness or goodness of-fit (GOF) results of the measurement model which was expected to present acceptable levels. Additionally, more scrutinised methods for determining convergent and discriminant validity as well as reliability of the model were adopted. These steps are presented in chapter 5, the empirical findings / statistical analysis of this study.
Final SEM Stages: 5 & 6
These two stages are forming the final steps of this study, and include the creation of the final SEM model and its validity checks. Chapter 6 elaborates on the creation of the final structural model, and explains how the final CFA model derived in the empirical findings/statistical analysis (Section 5), is transformed and explained in Structural Equation Modeling. It also provides a detailed analysis discussion of the final proposed model of this study, and explains what the new formed model and its constructs mean.
Data Collection
A survey was chosen in order for data to be collected for this study. After taking into consideration that the expected number of participants was around 200 and the participants were coming from different geographical locations and could be contacted through e-mail and social media platforms, the survey method was found to be the most appropriate to use. It was anticipated to use answers from 150-200 questionnaires to provide a reliable result. This thesis intended to attain a minimum sample size of 150 responses as it used a model with 5 constructs. Larger samples could contribute better results, but in the current study the recommended minimum sample of 150 is proposed for the number of SEM constructs used (Hair, et al., 2014).
A brief explanation of survey as well as explanations of the structure of the questionnaire used in this study will follow.
Surveys
Using surveys as a data collection method has a significant advantage of giving the possibility to collect data from many respondents and compare the results in an easy way (Miller & Brewer, 2003). In addition, as previously referred, the findings can be checked and duplicated by other researchers. Conducting a survey has also some drawbacks. For example, the participant of the survey may misunderstand some of the existing questions and this also may affect negatively the validity of the study (Miller & Brewer, 2003). The potential of not responding to surveys is considered as another problem of this data collection method. This concerns those participants that choose not to answer some specific questions (Miller & Brewer, 2003).
The survey used for the relevant study is of self-administrated type. Self-administrated questionnaires, most of the times are either on-line surveys or mail surveys (Bryman, 2012). These ways of spreading the survey were both used here. These types of surveys used, were time-effective and gave the possibility of reaching a greater number of respondents. Additionally, since it was difficult to reach the participants in other ways, it was easier to approach them through internet. Moreover, specifically these surveys can easily be modified and ease in answering them through text boxes (Bryman, 2012).
Descriptive Information on the Population Surveyed
The designed survey asked all the participants at the individual part, to indicate their gender, age group, educational level, work position and location of employment. In the analysis part, all the information gathered from the population that participated in the current survey will be further investigated.
Firstly, gender was measured as female or male. The respondents were called to choose among four age groups covering all the ages existing in a working environment starting from 18-30 and continuing by 31-40, 41-50 and up to 50 years old. In continuation, the educational background of the professionals was measured as High-school, 2-yr College, 4-yr College, Master Level and PhD Level. Regarding the location of employment, each respondent was free to write his/her own country since the questionnaire was spread to different countries (mainly European Countries).
Questionnaire
As previously indicated, this study used an electronic questionnaire to collect the data. This questionnaire aimed to gather data related to the leadership styles, creative process engagement, self-efficacy and innovative work behaviour. The data collected through the relevant questionnaire were also used to conclude if the different leadership styles, creative process engagement and self-efficacy have an impact on the reported innovative work behaviour.
The designed questionnaire has a closed question structure, and can be found at the end of this study in Appendix A. It was divided into three parts. The first section was created to explain to the participants the purpose and scope of this study, as well as ensuring them that all their personal information filled in the questionnaire would remain confidential.
The second part consisted of the demographic questions of the respondents, which includes, gender, age, education background, position and location of employment.
The third part contained questions for each one of the measured indicators, i.e. Ethical Leadership (EL), Participative Leadership (PL), Creative Process Engagement (CP), Self-Efficacy (SE) and Innovative Work Behaviour (IW), in which a five-stage scale is used to measure them. The scale presented the following ranges “1: Strongly disagree”, “2: Disagree”, “3: Neutral”, “4: Agree” and “5: Strongly agree”. This questionnaire was distributed online via email and other messaging services.
This survey concluded by urging all the respondents to forward the questionnaire to other candidates adequate to answer the same questions, in order to further maximize the number of responses and increase the validity of the results.
Missing Data
During the process of data collection, and when using a questionnaire as a data collection tool, the problem of missing data may arise (Gyimah, 2001). It was possible that the respondents may have skipped some of the questions in terms of minimizing the answering time or s/he may not observe some of the questions need to be filled. Also, it is a fact that some respondents may feel unsecure of sharing their personal opinions and details. Therefore, it was of great importance to try to minimize the missing data that could possibly occur for the current study.
For this reason, the questionnaire was spread in two formats. The first one consisted of a word document accompanied with a message emphasizing that the respondents should fill all the questions, since uncompleted questionnaires will hot help the analysis. This would eliminate the possibility of missing a question. The second one was an electronic questionnaire that limits the possibility of having missing data, since all the questions were mandatory to be filled in order to submit it. Hence, it was assured that all the submitted questionnaires were dully filled in. To conclude, both formats also contained a small text assuring the respondents that all their personal details would remain confidential.
4.
Survey Descriptive Results
Overview
This section summarizes the results of the data collection. Firstly, the demographic details of the respondents are presented in order to understand the background of the population that has been surveyed, and how the results relate to this population. Secondly, a data set was generated in order to perform a SEM analysis.
Respondent Demographics
All the respondents were contacted via email and explained the importance of the study together with the link leading them to a web-based questionnaire using “google forms” or an attached questionnaire document in word format. The survey was sent out to a broad network of professionals mostly from the United Kingdom, Greece and some other European countries (e.g. Sweden, Norway etc.). All the respondents of this study were full-time employees. Some of the participants voluntarily spread the survey to further assist in increasing the number of the respondents and enhancing the validity of this research study. After receiving the first completed questionnaires within the first three days, a reminder e-mail was sent to respondents in order to achieve as many collections as possible. The participating invitation was sent to just over 200 respondents. The collection period was ran for one week; during this time, 177 completed questionnaires were collected.
In this section, the demographic data are presented through graphs created with the help of Microsoft Excel, as well as with tables extracted from IBM SPSS analysis software.
Gender
The following bar chart illustrates the number of men and women participants. In this study of the 177 respondents in total, 79 of them were female, while the male participants 98 in total, found to be more in number than that of women workers (Figure 3).
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Age
For this survey the respondents had to choose among four age groups, 18-30, 31-40, 41-50 and over 50 years old. The highest percentage of 34% showed that the respondents belong to the first age group of 18-30 years. The second higher of 29% corresponds to the second age group of 31-40. The percentages of the two last age groups were almost similar, since workers of 41-50 years old represented the 19% of the population and professionals up to 50 years old almost the 18% (Figure 4).
Figure 4 Age
Location of Employment
Geographical locations of the working environment are identified in this questionnaire. Focusing on European respondents, the majority of them found to be from Greece by 58%, followed by United Kingdom with a percentage of 29% and of 6% from other countries. The high percentage of Greece and the United Kingdom was expected, as persons within those two countries were active in their support in the process of data collection. Participants from Sweden acquire the 4% of the surveyed population, while professionals from Norway obtained the smallest percent of 3% (Figure 5).
Figure 5 Location of Employment
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Educational Level
Besides the location of employment, it is important to understand the level of education of the participants, hence this questionnaire is also designed to capture this data. The respondents came from a variety of industries in the technological sector. In terms of their educational level, almost more than the half answered that they obtain a Master with a percentage of 51%. The second larger percentage of 40% of the population answered that they have accomplished their studies in a 3/4-year College. A small amount of 4% of the participants obtain the higher university degree (PhD level). The 3% of the respondents answered that they have a high-school degree and only a small minority of 2% found to have a 2-year college degree (Figure 6).
Figure 6 Educational Level
Position
Another important characteristic to be examined is the position of the respondents within their companies. The relevant survey tries to cover a range of positions existing in technological firms, for which the surveyed professionals are employed. The pie chart of Figure 5 illustrates the different positions of the participants. Belonging to Management accounted for 24 per cent. Unsurprisingly, a large amount of people of 36%, has Engineering roles, considering that the surveyed population works mostly in technology sector. The 16% works in other positions different than those described in the questionnaire. A smaller amount of 11% works in Finance/Accounting while, the 8 per cent corresponds to Administration and the minor percent of 3% represents those working in R&D (Figure 7).
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5.
Empirical Results / Statistical Analysis
This section outlines the statistical analysis performed to evaluate the theoretical model. The analysis examines the model’s validity and reliability, and re-structures the model as required in a step-by-step fashion in order verify this study’s theory. The statistical analysis consists of the following steps:
1. Theoretical SEM Model Construction 2. SEM theoretical model Analysis
3. Confirmatory Factor Analysis of Theoretical SEM Model 4. Exploratory Factor Analysis
5. CFA Model Analysis and Corrections 6. Final CFA Model
7. Construct Validity and Reliability
Theoretical SEM Model Construction
For the purpose of this study, a model of five constructs was created, where each construct correlates to specific questions in the questionnaire. The twenty-seven closed-ended questions in total are directly related to the constructs provided through the questionnaire. All data collected were analysed with IBM SPSS software for obtaining statistical results. A further analysis was conducted with AMOS software to gain insight of how the constructs interacted among them.
The data was adjusted for input to SPSS for performing analysis of the SEM model. A unique code was created for each question used from the five constructs (also mentioned in the Theoretical framework part of this study). The demographic questions were used as “non-numerical” in the SPSS program. The codes used for the five constructs of the SEM Model as presented below:
¾ Ethical Leadership : Question 1 = EL1, Question 2 = EL2, Question 3 = EL3, Question 4 = EL4, Question 5 = EL5
¾ Participative Leadership : Question 1 = PL1, Question 2 = PL2, Question 3 = PL3, Question 4 = PL4, Question 5 = PL5, Question 6 = PL6, Question 7 = PL7
¾ Self-Efficacy : Question 1 = SE1, Question 2 = SE2, Question 3 = SE3, Question 4 = SE4, Question 5 = SE5
¾ Creative Process Engagement : Question 1 = CP1, Question 2 = CP2, Question 3 = CP3, Question 4 = CP4, Question 5 = CP5
¾ Innovative Work Behaviour : Question 1 = IW1, Question 2 = IW 2, Question 3 = IW 3, Question 4 = IW 4, Question 5 = IW5
Note: All questions can be found more detailed in Appendix A.
The SEM model was created by categorizing the constructs to exogenous and endogenous. Ethical Leadership and Participative Leadership considered to be exogenous since they are not dependent on one another. This means that they have correlational relationships with other constructs and act as independent variables in structural relationships (Hair, et al., 2014). The mediator constructs (Creative Process Engagement and Self-Efficacy) were considered to be endogenous as they are indicating a dependence relationship. Innovative work behaviour is classified as an endogenous parameter as well.
Table 1: Constructs Description
Exogenous Constructs Endogenous Constructs
Ethical Leadership Creative Process Engagement
Self-Efficacy Participative Leadership
Innovative Work Behaviour
After defining the relationships and path diagram of the model, the data were incorporated into the program in a format suitable for analysis. Firstly, this analysis aimed to estimate the strength of the aforementioned relationships. Secondly, it targeted to show how the constructs of the designed model were associated with each other for testing and proving the theoretical hypotheses. The theoretical model is shown in Figure 8.
SEM Theoretical Model Analysis
After inputting the questionnaire data in Amos, the first step was to run the theoretical model as Shown in Figure 8. In order to examine its normality. Figure 9 shows the SEM model and all the relationships (loadings) as appear after calculating estimates. Before proceeding to the actual analysis techniques presented in next section, a check of normality of data was performed at this stage for the purpose of evaluating the model at this early stage.
Figure 9 Theoretical SEM model first run
Assessment of Normality for the SEM Theoretical Model
Normality of data check determines whether the examined dataset is well demonstrated by a normal distribution. If the dataset is modelled correctly, the researcher will be enabled to evaluate and make suggestion to the random samples tested from the overall population (Bryman & Bell, 2007).
The results of assessment of normality are presented in Table 2 below. The first column represents the variables and corresponds to all the questions in the questionnaire, whereas the second and third columns
denote the maximum and minimum recorded values of the replies gained. The Skew and Kurtosis values also shown in columns 4 and 6 respectively.
When a Kurtosis value is 0, it means that the dataset has perfect normality. Generally, Kurtosis values between -3.0 and +3.0 can be deemed acceptable (Kline, 2010). Though, values between -2.0 and +2.0 are satisfactory when showing normal univariate distribution (George & Malley, 2010). All of the Kurtosis values presented below are within the acceptable limits. Thus, it can be assumed that the collected dataset has acceptable normality.
Column 5 (C.R.) represents the T-Values which will be examined during the CFA analysis of this thesis. Three values are above 5 and are highlighted in amber colour; as according to Bentler & Byrne (Bentler, 2005) (Byrne, 2009) the C.R values > 5.0 indicate non normality. This indication showing that the 3 variables need to be deleted from the model.
Table 2: Assessment of normality Theoretical SEM Model
